The long-term goal of this research grant is to bring new developments in statistical theory and methodology to bear on the practical problems of biostatistics and medicine. The researchers pursue this goal from their joint positions in the Stanford medical school, where they work on specific biostatistical projects, and the development and review. Research on the grant involves the creation of new statistical methods, study of new and existing techniques through mathematical analysis and computer simulation, and trial applications of these procedures to ongoing research projects at the medical school. The following five areas will be the focus of the research: (1) bootstrap methods, (2) recursive partitioning and classification trees, (3) modern regression methods and their extensions, (4) image reconstruction methods, (5) hazard rates and censored data. Successful new statistical methodology finds application across a wide spectrum of biomedical applications. On the basis of past experience, the topics studied here will be particularly useful in cancer therapy clinical trials, cardiovascular research, and areas like gait analysis where diagnostic measures tend to have very complicated structure.